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Data mining techniques utilizing latent class models to evaluate emergency department revisits.

Ofir Ben-Assuli1, Joshua R Vest2

  • 1Faculty of Business Administration, Ono Academic College, Kiryat Ono 55000, Israel.

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|November 21, 2019
PubMed
Summary

Machine learning models effectively forecast emergency department (ED) revisits by analyzing patient data over time. Utilizing hidden Markov models (HMMs) for pre-analysis significantly improved prediction accuracy for these repeat ED visits.

Keywords:
Electronic health recordsEmergency department revisitHealth information exchangeHidden Markov ModelsPredictive analytics

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Predictive Analytics

Background:

  • Emergency departments (EDs) face complex challenges, including potentially inappropriate resource utilization.
  • Repeat ED visits (revisits) represent a key area for resource optimization and predictive modeling.

Purpose of the Study:

  • To forecast emergency department (ED) revisit risk over time using latent class models.
  • To apply hidden Markov models (HMMs) for tracking patient risk trajectories and predicting future ED revisits.

Main Methods:

  • Integrated data from four sources, including electronic health records and health information exchange.
  • Developed four HMMs to model the relationship between observed data and hidden patient states over time.
  • Employed latent class analysis for patient pre-analysis before applying various data mining classifiers.

Main Results:

  • Pre-analysis using latent class models and HMMs significantly enhanced the performance of all prediction models.
  • The approach demonstrated superior predictive accuracy compared to models without HMM pre-analysis.

Conclusions:

  • Leveraging the longitudinal nature of healthcare data is a promising strategy for advanced risk prediction.
  • Exploiting patient state variation within healthcare data can improve the forecasting of ED revisits.